Figure shows the processing sequence applied to the data to alleviate the influence of noise and event-crossing in the velocity inversion. Although muting could be used to eliminate event-crossing, this would result in the discharge of useful information from a particularly important part of the reflections. Multiples and other coherent noise were attenuated by filtering techniques while the problem of interference between reflections was solved by a differential treatment of the shallow reflections.
It is clear from Figure a that despite the application of predictive deconvolution and a dereverberation algorithm (described in Cunha and Muir, 1989), complex multiples and peg-legs are still dominant features in the data. More effective in the elimination of the multiples and linear noises was the application of a filter in the domain. The direct and inverse transform were performed by an algorithm developed by Kostov (1989); Figure a shows the shape of the cutoff curve. The high-slowness part of the shallow reflections is located inside the rejecting region to avoid the reflections interfering in the subsequent processing of the reflections below them. Figure b shows the filtered data transformed back to the x-t domain.
A comparison between the velocity spectra of the data before and after the filtering (Figure ) shows a good improvement in the resolution of primary events after the filtering. The times of the picks associated with primaries were used as the starting points for the event-detection algorithm. The dashed line in Figure b defines the cutoff velocity function for the velocity filter that was applied to the beam-stacked data. The application of this filter is essential to avoid the interference of multiples, not only in the event-picking algorithm, but also in the final inversion step.
A total of sixteen events were selected for the inversion. Their a priori nearest-offset times were selected from the velocity spectrum, but the event-picking algorithm had a pre-specified degree of freedom to reestimate them. The four initial events (which cross the reflections below) were selected with the help of a normal-moveout overlay program (Claerbout, 1987).
The last step before the velocity estimation algorithm selects the slices along the event-mapped surfaces in the beam-stack cube (Figure b). The slices corresponding to the shallow events are taken from a non-filtered version of the beam-stack cube. To optimize the estimation, the beam-stack of the shallow events (1 to 9) was computed apart from the deep events (9 to 16), so that a lower slowness interval could be used for the latter.